Fairness Enhancement Using Transmission Scheduling ... - IEEE Xplore

The 3rd International Conference on Communications and Information Technology (ICCIT-2013): Wireless Communications and Signal Processing, Beirut

Fairness Enhancement Using Transmission Scheduling in Multi-Hop Wireless Mesh Networks Jalaa Hoblos Department of Computer Science Hiram College Hiram, Ohio 44234

Abstract—A wireless mesh network (WMN) is a peer-topeer multi-hop wireless network in which nodes cooperate with one another to route packets to the gateway. In multi-hop wireless networks, flows spanning multiple hops experience more contentions in order to access the medium compared to those closer to their destination. Thus, nodes with longer distance to the gateway suffer from bandwidth starvation. In this paper, we propose a transmission scheduler algorithm that achieves better fairness and maximizes bandwidth use. Our algorithm assigns each node a weight based on its location in the WMN and on its aggregated traffic load. The weight is later used to compute each node transmission time. We show that the algorithm improves considerably the throughput of each node in the network and the overall network throughput. In addition, it enhances bandwidth fairness among nodes, and decreases the end-to-end average delay. The results are evaluated by means of simulations.

I. I NTRODUCTION Wireless mesh networks have evolved considerably in recent years in various technologies such as Wi-Fi, WiMax, sensor and ad hoc networks. Researchers have focused on their applications including broadband home networking, enterprise networking, campus and community networking. A wireless mesh network consists of one or more wired gateways to the Internet, a number of wireless access points (APs) that relay client’s traffic via other wireless APs towards the gateway and other wireless clients. Generally, APs act as wireless routers (relay nodes) and normally participate in forming a minimum spanning tree rooted at a gateway. Most WMNs are based on Wi-Fi and WiMax technologies and use one or more of the IEEE 802.11x [1], IEEE 802.15x and 802.16x standards. Although WMNs are flexible and easy to deploy, they lack scalability. The throughput falls considerably as the number of hops increases. This is mainly due to traffic aggregation, and saturation tree build up towards the gateway. Cross-layer optimization of existing protocols from the application layer, to the transport layer, to the MAC layer, and to the physical layer are needed to satisfy the quality of service (QoS) requirements [2]. The nature of the spatial contention for a wireless channel and the spatial reuse of the channel bandwidth introduce a conflict between optimizing aggregate allocated bandwidth and achieving fairness [3]. In addition, in the absence of a centralized control, achieving such fairness becomes far more challenging. Generally, CSMA/CA including 802.11 family protocols does not work well in multi-hop wireless networks. This is

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mainly due to the hidden and exposed node problems, and successive binary exponential back-off contention resolution algorithm [4]. It has been shown [5] that current protocols that are adapted for WMNs can lead to starvation for flows farther away from the gateways. In this paper, we present a Transmission Scheduler Algorithm (TSA) that improves fairness and throughput in multihop WMNs. We compare our results with the results of the default scheduling technique. We refer to the default scheduling technique as the technique where all nodes begin transmitting at the same time (no scheduling is implemented). Our algorithm outperforms the default scheduler in terms of throughput, fairness, and end-to-end delay. This paper is organized as follows. Section II covers major research work in fair bandwidth allocation in multi-hop wireless networks. Section III explains the network model used in this work and the proposed TSA. Section IV describes simulation implementation, and assumptions. In Section V we study the performance of TSA. Section VI discusses our future work, and our conclusion. II. P REVIOUS W ORK The authors in [6] analytically proved and modeled the existence of starvation for the nodes located two hops away from the gateway in a linear topology. They showed that the starvation problem can be tackled by setting the contention window, of the nodes one hop away from the gateway, to a value much larger than the other nodes. However, their work did not include the exposed node problem. In [7], the authors presented a rate control mechanism in which nodes one hop away from the gateway control their transmission rates to let the less fortunate others share the gateway resources. In [5], fairness and end-to-end performance in multi-hop wireless backhaul networks has been studied by designing an ideal reference model that defines the performance objectives such as removing spatial bias and maximizing spatial reuse. A distributed algorithm has been used to show the capability of achieving fairness without any modifications of the transport protocol. In [8], the authors implemented a fair rate control that approximate and divide that available capacity among contending flows.

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In [9], the authors introduced an algorithm to improve fairness in WMNs. Their algorithm is based on changing the contention window size of each node to distinguish the medium access probability for each user. In [10], the capacity of WMNs has been formulated in terms of collisions domain, which was defined as the set of nodes that need to be inactive in order for the wireless link to transmit successfully. It has been shown that the effective load on a collision domain is less than or equal to the sum of traffic on its links. A constant effective MAC capacity has been assumed in this study which may not be realistic due to the interference and other physical limitations in multi-hop wireless networks. A heuristic space time division multiple access (STDMA) algorithm has been proposed in [11] to improve the throughput and fairness in WMNs. The technique is difficult to implement mainly due to the centralized nature of STDMA and complexities associated with time synchronization along the paths in multi-hop networks [2]. In [12], fairness has been improved through a queue management technique in which an arriving packet at each mesh point is either admitted into the queue or dropped, depending on its source node granted buffer quota. A centralized traffic congestion control algorithm to mitigate unfairness has been proposed in [13]. A single path routing has been used that may not be scalable on a multi-path routing due to a large number of potential paths a flow can choose. Some features of IEEE 802.11e like TXOP (Transmission Opportunity), and CW (minimum Contention Window size) adjustment, similar to the approach in [9], has been used to restore fairness at relay aggregation points. It has been observed in [14] that, in multi-hop wireless networks, flows with the same hop count may experience different throughput (symmetrical unfairness). The authors proposed a distributed routing algorithm to improve this symmetrical unfairness. However, it is not clear if the algorithm can be used to remove spatial bias. The pros and cons of different queuing techniques to examine fairness in multi-hop wireless networks have been studied in [15]. It has been observed that schemes that provide fairness require per-flow queuing, which comes at the expense of bandwidth efficiency. It was also observed that optimal bandwidth utilization can not be achieved without a MAC layer that differentiates priorities. A distributed slot allocation algorithm has been used in [16] to improve fairness in multi-hop wireless networks. While the scheme did not make any assumption about the existence of a centralized coordination of traffic patterns, a 2-hop interference region has been assumed. This is not necessarily the case in WMNs. The authors in [17] showed that the maximum achievable throughput in multi-hop wireless Ad Hoc networks varies inversely with mean path length, node density, and communication radius of nodes. To achieve fairness, the authors in [18] proposed a method to assign variable transmission rates to relay nodes based on their aggregate Erlang-B blocking probabilities. They regu-

lated traffic flows to the gateway in such a way that achieved fairness. However, Fairness was obtained at the expense of lower overall network throughput. III. N ETWORK M ODEL AND T RANSMISSION S CHEDULER A LGORITHM A. Network Model In this paper, we consider static multi-hop wireless mesh networks consisting of N nodes and one gateway g to the Internet. Each node, except g, sends and relays packets to the gateway. As soon as a packet is generated by a node, it is relayed from a node to another along the way until it reaches its destination (g). We assume that g is aware of the network topology it serves. We also suppose that all nodes generate the same traffic load (homogeneous). The network employs the IEEE 802.11 DCF with RTS-CTS handshake protocol. B. Transmission Scheduler Algorithm (TSA) A successful transmission of node i in a multi-hop wireless mesh network is affected mainly by three key factors: 1) The number of hops hi , node i is away from the gateway 2) The aggregated traffic load fi at node i including i’ own traffic 3) The number of nodes Ii in i transmission range (interfering nodes) a) Definitions: : • Γ is defined as the set of all nodes in the network excluding the gateway g Γ = {i}, ∀i = 1, . . . , N, g ∈ /Γ The weight of node i is defined by wi The transmission time τi of node i is defined as the time scheduled for i to starts transmitting its own packets towards g b) Weight Computation: : We compute the weight wi of node i based on the three factors listed above as follows: • •

wi = phi ∗ hi + phi ∗ fi + pIi ∗ Ii , ∀i ∈ Γ

(1)

where phi , p fi , and pIi are the weighted powers of: i) the number of hops hi , ii) the aggregated traffic load fi , and iii) of the number of interfering nodes Ii , of node i respectively. As examined by [6], [5], [12], the number of hops away (hi ) a node i is from the gateway has more effect on a node individual throughput than the other two factors. In addition, we conduct an intensive number of simulations, and we conclude that fi has also more effect on the node performance than Ii . Thus, we estimate phi , p fi and pIi as follows: phi = 2 ∗ p fi (2) pIi = .5 ∗ p fi Eq 1 can now be rewritten as: wi = 2 ∗ p fi ∗ hi + p fi ∗ fi + .5 ∗ p fi ∗ Ii = p fi (2 ∗ hi + fi + .5 ∗ Ii )

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(3)


we assume a homogeneous traffic for all nodes as indicated in Section III-A, thus p fi is the same ∀i. For the rest of this paper, we will use p instead of p fi to simplify the notation. c) Numerical Example: : To give an example on how we compute the weights, consider the WMN of Figure 1. As shown in Figure 1, node 2 for instance is 1-hop away from the gateway (h2 = 1), it generates its own traffic and forwards traffic for 3 other nodes ( f2 = 4), and it has 3 nodes in its transmission range (I2 = 3)

Note that the transmission times overlap most of the time. For instance, assume that τi ≤ τ j ≤ τk ≤ τl . . . ≤ τs . The time interval t where nodes are transmitting is shown in Eq 6.

(4)

 τi ≤ t < τ j , only one is transmitting     τ j ≤ t < τk , Two nodes are transmitting    τk ≤ t < τl , Three nodes are transmitting :     :    t ≥ τs , all nodes in the network are now transmitting

Following the same reasoning, the weight of each node in Figure 1 is then computed as follows:

IV. S IMULATION A SSUMPTIONS AND E XPERIMENTS

wn2 = p ∗ (2 ∗ 1 + 4 + .5 ∗ 3) = 7.5 ∗ p

Fig. 1: A Line Mesh Network

 w2 = 2 ∗ p + 4 ∗ p + 1.5 ∗ p = 7.5 ∗ p    w3 = 4 ∗ p + 3 ∗ p + 2 ∗ p = 9 ∗ p w4 = 3 ∗ p + 2 ∗ p + 1.5 ∗ p = 9.5 ∗ p    w5 = 4 ∗ p + 1 ∗ p + 1 ∗ p = 10 ∗ p d) Transmission Time Computation: : The nodes transmission times are computed by using the weights obtained earlier as follows:

τk = 0, τi = ξi ∗ α,

k is the node with maximum weight ∀i ∈ Γ, and i 6= k

(6)

We assume that the network is always saturated, i.e. nodes always have packets to send to the gateway. We assume that TSA is implemented at the gateway g, thus g is in charge of computing each node’s weight and transmission time. The carrier sense range is set to 339m and the distance between nodes in both networks is set to 170m. We use static routing to inflict one way routing. The speed of the channel is set to 1 Mbps, and the packet size is set to 1024 bytes. All nodes share a single communication channel. Clients send UDP packets over the channel. We use the IEEE 802.11s mesh network protocol implemented in QualNet (version 4.5) simulation software to evaluate the performance of the TSA. We assume that the traffic at each node is generated according to an i.i.d Poisson process. We run the simulation over three different source traffic loads: 273 Kbps, 409 Kbps, and 819 Kbps. We compute each node throughput as follows: throughput =

bits received finish time - start time

A. Choosing α (5)

where α is a small time window, and ξi is the normalized wi of node i. In other words, the transmission times of nodes is directly proportional to their normalized weights. Section IV-A is devoted to discuss how to choose the value of α. TSA is presented in algorithm 1.

By intuition, when the load in the network increases, the time interval t between two nodes must also increase to assure better bandwidth use and fairness. To increase t means that the value of the time window α must also increase. Proposition 1: : α is directly proportional to t Proof: : Let’s suppose that nodes ni and j are transmitting consecutively i.e ξ j > ξi . Using Eq 5 and Eq 6 we have:

Algorithm 1 Transmission Scheduler Algorithm (TSA) 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12:

Giving Γ, α for i ∈ Γ do Compute wi Compute ξi {Normalize wi } end for for i ∈ Γ do if ξi 6= 1 then τi = ξi ∗ α else τi = 0 end if end for

t

= α ∗ (τ j − τi )

Thus, α=

t (τ j − τi )

(7)

The denominator (τ j − τi ) is a constant, thus, α is directly proportional to t. For the traffic loads of 273 Kbps, 409 Kbps, and 819 Kbps proposed in Section IV, α is estimated as 30s, 40s, and 50s respectively. Note that even smaller values of α still gives better performance than that of the default scheduling but slightly decreases fairness between nodes in the network.

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(a) Node 2 Throughput

(b) Node 3 Throughput

(a) Nodes Throughput using Default

(b) Nodes Throughput using TSA

Fig. 3: Nodes Throughput Using both Techniques on the Network of Figure 1

(c) Node 4 Throughput

(d) Node 5 Throughput

Fig. 2: Nodes Throughput of the Network of Figure 1 (a) Average end-to-end Delay

V. TSA P ERFORMANCE We apply TSA on two mesh networks to evaluate its performance: a line network and a ring network. The line network, mentioned previously in Section III, consists of 5 nodes, in which 4 nodes generate traffic and node 1 is the gateway as shown in Figure 1. In the ring topology, we assume a mesh network of 6 nodes where 5 out of the 6 nodes generate traffic and node 6 is the gateway as shown in Figure 5. We also assume that node 2 sends its traffic toward the gateway through node 1. A. Line Mesh Network The simulation results show big improvements of the TSA over the default technique. Figure 3 shows that TSA achieves better fairness than its counterpart. The discrepancy between nodes’ throughput is minimized when using TSA as indicated by Figure 3. TSA also improves node 5(farthest node) throughput by about 145% compared to that of the default technique as shown in Figure 2d. In addition, TSA improves node 4 throughput by almost 76% as indicated by 2c. Figures 2b and 2a show that TSA improves as well nodes 3 and node 2 by about 45% and 24% respectively . The overall network throughput increases by about 57% when using the TSA as indicated by Figure 4b. Last, the TSA decreases the average end-to-end delay by about 36% as shown in Figure 4a.

(b) Overall Network Throughput

Fig. 4: Average end-to-end Delay and Overall Throughput of the Network of Figure 1

1 throughput by almost 25% as indicated by 8a. Additionally, Figure 8d and Figure 8e show that TSA improves nodes 4 and node 5 throughput by about 5% and 11% respectively. The throughput of node 3 is also increased by about 48% as indicated by Figure 8c. Figure 7a shows that TSA decreases the average end-to-end delay by about 23%. Last, the overall network throughput also increases by about 20% when using the TSA as indicated by Figure 7b. VI. D ISCUSSION AND C ONCLUSION Multi-hop networks based on IEEE 802.11 suffer from providing fair access to flows traveling with an increasing number of hops. In this paper, we present a Transmission Scheduler Algorithm (TSA) to improve throughput and fairness in WMNs. TSA assigns each node a weight based on its location and its aggregate traffic load. It then uses the weight

B. Ring Mesh Network TSA is also applied on the network of Figure 5. Results show significant improvement over the default technique. As indicated by Figure 6, TSA achieves better fairness than its counterpart. TSA also improves node 2 (farthest node) throughput by about 81% compared to that of the default technique as shown in Figure 8b. In addition, TSA improves node

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Fig. 5: Multi-Hop Mesh Network


studying the possibility of finding an optimum time window α that optimizes fairness and throughput in multi-hop wireless mesh networks. R EFERENCES

(a) Nodes Throughput using Default

(b) Nodes Throughput using TSA

Fig. 6: Nodes Throughput Using both Techniques on the Network of Figure 5

(a) Average end-to-end Delay

(b) Overall Network Throughput

Fig. 7: Average end-to-end Delay and Overall Throughput of the Network of Figure 5

to compute the transmission time for a node. Preliminary simulation results show that the TSA outperforms the default scheduler in terms of individual nodes’ throughput, fairness, overall network throughput, and the average end-to-end delay. In this work, we assume that the traffic in the network is homogeneous, therefore the value of the time window α is set to be the same for all nodes in the network. In the future, we are planning on expanding our research to include computing the suitable time window α for each node in the network when the traffic in the network is heterogeneous. We are also

(a) Node 1 Throughput (b) Node 2 Throughput (c) Node 3 Throughput

(d) Node 4 Throughput (e) Node 5 Throughput

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Fig. 8: Nodes Throughput of the Network of Figure 5

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